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AI Consulting Services
Before anyone writes a prompt, someone should establish which processes are worth automating, whether the data supports it, and what the payback actually looks like. That is the work this engagement does.
The expensive mistakes this engagement prevents
Most AI budget is wasted before development starts — on the wrong use case, the wrong sequencing, or a build that duplicates something a tool already does well.
We have seen organisations spend six figures automating a process that ran eleven times a month, and others delay a genuinely transformative build for a year because nobody could get the business case past finance.
A consulting engagement is deliberately vendor-neutral. If the recommendation is to buy rather than build, or to fix your data pipeline before touching AI at all, that is what the report says.
- Automating a low-volume process with no payback
- Starting with the hardest use case instead of the most winnable
- Building what a $40/month tool already does
- Committing to one model provider before requirements are clear
- Discovering a data-residency blocker after development
- No agreed definition of "good enough" to measure against
What the engagement covers
Scoped to your situation. A focused readiness review runs two to three weeks; a full multi-department roadmap runs six to eight.
AI readiness assessment
Data quality and accessibility, systems and API maturity, internal skills, governance posture and change-readiness — scored, with gaps named.
Use-case discovery
Structured workshops with the people who run the process, not just the people who sponsor the project. Typically surfaces 15–40 candidates.
ROI analysis
Volume, handling time, error rate and fully-loaded cost per transaction against projected AI cost and expected automation rate.
Prioritisation matrix
Every candidate scored on business value and technical feasibility, plotted, and sequenced into waves.
Solution architecture consulting
Reference architecture for the priority use cases: data flow, retrieval design, tool boundaries, hosting and failure modes.
LLM selection
Benchmarked against your actual tasks and data, comparing accuracy, latency, residency and cost per transaction — not vendor marketing.
RAG strategy
Which knowledge sources, what ingestion cadence, how permissions map, and what accuracy threshold makes it deployable.
Agentic AI strategy
Where autonomy is appropriate, where it is not, and what approval boundaries each agent needs.
Implementation roadmap
A sequenced 6–18 month plan with phases, dependencies, team shape, budget envelope and decision gates.
AI governance
Model registry, approval policy, acceptable-use guidance, incident process and review cadence.
AI security review
Data flow mapping, PII exposure analysis, prompt-injection risk, access control and third-party endpoint assessment.
AI modernisation planning
How to add intelligence to legacy systems without a replacement programme you cannot fund.
How the engagement runs
Fixed scope, fixed fee, fixed end date.
- You own every deliverable outright
- Vendor-neutral: buy, build or do nothing are all valid outcomes
- No obligation to implement with Ezulix
- Consulting fee credited against a subsequent build engagement
Ten stages from first call to a system your team trusts
Every AI engagement runs this sequence. Small projects compress stages; regulated projects expand them. Nothing gets skipped silently.
Discovery
A working session with your operations and engineering leads to map the process, the systems it touches, and where the cost actually sits.
AI Opportunity Assessment
We score candidate use cases on data readiness, volume, error tolerance and payback, then rank them. Some come back "do not use AI for this" — you get that answer too.
Solution Architecture
Model selection, retrieval design, tool boundaries, data flow, failure modes and hosting topology, documented before code.
Proof of Concept
A narrow build against your real data to prove accuracy on the cases that matter, typically 2–4 weeks. Go / no-go decision at the end.
MVP
One workflow, end to end, in the hands of real users. Evaluation sets and quality thresholds are defined here, not retrofitted.
Production Development
Hardening: error handling, retries, fallbacks, cost controls, rate limits, observability, and a human escalation path for every automated decision.
Integration
Wiring into your CRM, ERP, HRMS, data warehouse, ticketing and messaging channels through APIs, webhooks and event queues.
Security Testing
Prompt-injection testing, access-control verification, PII handling review, dependency scanning and penetration testing before go-live.
Deployment
Staged rollout on your cloud or ours, with CI/CD, versioned prompts and models, and rollback in place from day one.
Monitoring & Optimization
Quality dashboards, drift detection, cost-per-transaction tracking and a retraining or re-prompting cadence agreed in writing.
Security-conscious architecture, from the first design review
Enterprise AI fails on governance more often than on models. Every system we build is designed to support enterprise security requirements and to give your risk team answers rather than assurances.
Data privacy & residency
Your data stays in the region and tenancy you nominate. We architect for no-training-on-your-data configurations and document exactly which vendor endpoints see which fields.
Role-based access control
Retrieval and tool permissions inherit your existing roles. A user cannot surface a document through the AI that they could not open directly.
Authentication & authorization
SSO via OIDC/SAML, short-lived tokens for agent tool calls, and per-tool scopes so an agent holds the narrowest possible privilege.
Encryption
TLS in transit, AES-256 at rest, managed keys via your cloud KMS, and encrypted vector stores for embedded content.
API security
Gateway-level authentication, signed webhooks, IP allowlisting, request validation and quota enforcement on every exposed endpoint.
Audit logging
Every prompt, retrieval, tool call, model version and human override is logged with a trace ID, so any output can be reconstructed months later.
Data isolation
Per-tenant separation at the storage, index and key level for multi-entity groups and regulated environments.
Secure prompt handling
System instructions are server-side, user content is treated as untrusted input, and we test against prompt-injection and tool-abuse patterns.
PII protection
Detection, masking or tokenisation of personal data before it reaches a model, with configurable redaction policies per field.
Human approval workflows
High-impact actions — payments, refunds, contract sends, record deletion — route to a named approver instead of executing autonomously.
Monitoring & anomaly detection
Alerting on unusual tool usage, cost spikes, refusal rates and quality regressions.
Rate limiting & abuse control
Per-user and per-tenant throttles, spend caps and circuit breakers so a runaway loop cannot become a runaway invoice.
Secure deployment
Private networking, secrets in a managed vault, immutable builds, dependency scanning, and infrastructure as code.
Questions enterprise buyers ask us first
What is an AI readiness assessment?
What does AI consulting cost?
Will you recommend buying instead of building?
Who needs to be involved from our side?
Do we own the deliverables?
Talk to an AI solution architect
No junior sales rep, no discovery deck. The person on the call is the person who will design the system.
- Response within one business day
- Mutual NDA signed before detailed discussion
- Written scope, one price, one delivery date
- You own all source code, models and IP at launch
Start with the assessment, not the build.
A 45-minute session to scope the engagement. You will leave with an initial view of where AI is likely to pay back in your organisation and where it will not.